# -*- coding:utf-8 -*-
# mnist_app.py
# coding:utf-8
from PIL import Image, ImageFilter
import tensorflow as tf
import matplotlib.pyplot as plt


def imageprepare():
	im = Image.open('home/xhh/桌面/tensorflow/3.png')
	plt.imshow(im)
	plt.show()
	im = im.convert('L')
	tv = list(im.getdata())
	tva = [(255 - x) * 1.0 / 255.0 for x in tv]

	return tva


result = imageprepare()
x = tf.placeholder(tf.float32, [None, 784])

y_ = tf.placeholder(tf.float32, [None, 10])


def weight_variable(shape):
	initial = tf.truncated_normal(shape, stddev=0.1)
	return tf.Variable(initial)


def bias_variable(shape):
	initial = tf.constant(0.1, shape=shape)
	return tf.Variable(initial)


def conv2d(x, W):
	return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
	return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1, 28, 28, 1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

saver = tf.train.Saver()

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())
	saver.restore(sess, "/home/xhh/桌面/tensorflow/model.ckpt")

	prediction = tf.argmax(y_conv, 1)
	predint = prediction.eval(feed_dict={x: [result], keep_prob: 1.0}, session=sess)

	print('识别结果:')
	print(predint[0])


